Loading the Packages

First we load the tidyverse() library of R packages (Wickham et al. 2019), which includes the ggplot2() package (Wickham 2016), the dplyr() package (Wickham et al. 2022), and the plotly() package (Sievert 2020)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.5.0 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## 
## Attaching package: 'plotly'
## 
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## 
## The following object is masked from 'package:stats':
## 
##     filter
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## The following object is masked from 'package:graphics':
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##     layout

Loading the data

Next we load our .csv data into R as a tibble object. The tibble has four columns: “data_souce, estimate_method, estimated_tfr, estimated_year”

data = read_csv("data/tfr_estimates.csv") %>%
  as_tibble()

## Rows: 549 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): data_source, estimate_method
## dbl (2): estimated_tfr, estimated_year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Plot Scatter

Finally, we use ggplot2() to compute a static data visualisation

# create the scatter plot
p = ggplot(data, aes(x = estimated_year,
                 y = estimated_tfr,
                 color = interaction(estimate_method,
                                     data_source))) +
  geom_point() +
  geom_line() +
  labs(x = "Estimated Year",
       y = "Estimated TFR",
       title = "Scatterplot of TFR Estimates of Palau 1950-2020",
       color = "") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.box="vertical", legend.margin=margin()) +
  guides(color=guide_legend(nrow=15, byrow=TRUE))

p

Make interactive

Using the ggplotly package , we can make the plot

# gonvert the ggplot to plotly
interactive = ggplotly(p) %>% layout(showlegend = FALSE)

interactive

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